R: Simulate and select null distributions on empirical gene-gene...

pcn {diceR}

R Documentation

Simulate and select null distributions on empirical gene-gene correlations

Description

Using a principal component constructed from the sample space, we simulate null distributions with univariate Normal distributions using pcn_simulate. Then a subset of these distributions is chosen using pcn_select.

Usage

pcn_simulate(data, n.sim = 50)

pcn_select(data.sim, cl, type = c("rep", "range"), int = 5)

Arguments

`data`	data matrix with rows as samples, columns as features
`n.sim`	The number of simulated datasets to simulate
`data.sim`	an object from `pcn_simulate`
`cl`	vector of cluster memberships
`type`	select either the representative dataset ("rep") or a range of datasets ("range")
`int`	every `int` data sets from median-ranked `data.sim` are taken. Defaults to 5.

Value

pcn_simulate returns a list of length n.sim. Each element is a simulated matrix using this "Principal Component Normal" (pcn) procedure.

pcn_select returns a list with elements

ranks: When type = "range", ranks of each extracted dataset shown
ind: index of representative simulation
dat: simulation data representation of all in pcNormal

Author(s)

Derek Chiu

Examples

set.seed(9)
A <- matrix(rnorm(300), nrow = 20)
pc.dat <- pcn_simulate(A, n.sim = 50)
cl <- sample(1:4, 20, replace = TRUE)
pc.select <- pcn_select(pc.dat, cl, "rep")

[Package diceR version 1.0.3 Index]